Search

Your search keyword '"Deresh Ramjugernath"' showing total 6 results
Start Over Author "Deresh Ramjugernath" Journal petroleum science and technology
6 results on '"Deresh Ramjugernath"'

1. Characterization of C7+ fraction properties of crude oils and gas-condensates using data driven models

Author

Amir H. Mohammadi, Deresh Ramjugernath, and Arash Kamari

Subjects
General Chemical Engineering, 0211 other engineering and technologies, Analytical chemistry, Energy Engineering and Power Technology, Fraction (chemistry), 02 engineering and technology, General Chemistry, Geotechnical Engineering and Engineering Geology, complex mixtures, Characterization (materials science), chemistry.chemical_compound, Fuel Technology, 020401 chemical engineering, chemistry, 021105 building & construction, Petroleum, 0204 chemical engineering, Specific gravity
Abstract

Petroleum fractions, in particular heptane-plus fraction (C7+), are complex mixtures and measuring properties of hydrocarbon-plus fractions is difficult and time-consuming. In this study, the norma...

Published
2019

2. Evaluation of wax disappearance temperatures in hydrocarbon fluids using soft computing approaches

Author

Deresh Ramjugernath, Amir H. Mohammadi, Alireza Rahimzadeh, and Arash Kamari

Subjects
Soft computing, chemistry.chemical_classification, Wax, Materials science, Petroleum engineering, General Chemical Engineering, 0211 other engineering and technologies, Energy Engineering and Power Technology, 02 engineering and technology, General Chemistry, Geotechnical Engineering and Engineering Geology, Fuel Technology, Hydrocarbon, 020401 chemical engineering, chemistry, Feed forward artificial neural network, visual_art, 021105 building & construction, visual_art.visual_art_medium, 0204 chemical engineering
Abstract

In this study, modeling wax disappearance temperatures (WDTs) in hydrocarbon fluids is presented using three different soft computing approaches, namely feed forward artificial neural network; mult...

Published
2019

3. An insight into thermodynamic consistency of hydrogen sulfide sulfur content data at isobaric conditions

Author

Saeideh Babaee, Deresh Ramjugernath, Hamed Hashemi, and Amir H. Mohammadi

Subjects
Work (thermodynamics), Materials science, General Chemical Engineering, Hydrogen sulfide, 0211 other engineering and technologies, Energy Engineering and Power Technology, Thermodynamics, Experimental data, 02 engineering and technology, General Chemistry, Geotechnical Engineering and Engineering Geology, chemistry.chemical_compound, Fuel Technology, 020401 chemical engineering, chemistry, Consistency (statistics), 021105 building & construction, Sour gas, Isobaric process, Sulfur content, 0204 chemical engineering, Reliability (statistics)
Abstract

In this work, a thermodynamic consistency test method was used to investigate the reliability of isobaric experimental data on the sulfur content of hydrogen sulfide. The test was applied t...

Published
2019

4. Can 2-methyl-2-butene and isoprene form clathrate hydrates?

Author

Paramespri Naidoo, Alireza Bozorgian, Amir H Mohammadi, Deresh Ramjugernath, and Kaniki Tumba

Subjects
General Chemical Engineering, Clathrate hydrate, 2-Methyl-2-butene, Energy Engineering and Power Technology, 02 engineering and technology, General Chemistry, 021001 nanoscience & nanotechnology, Geotechnical Engineering and Engineering Geology, chemistry.chemical_compound, Fuel Technology, 020401 chemical engineering, chemistry, Physical chemistry, 0204 chemical engineering, Hydrate dissociation, 0210 nano-technology, Isoprene
Abstract

In order to establish whether 2-methyl-2-butene and isoprene can form hydrates, experimental hydrate dissociation data were measured for the following systems: methane + water, 2-methyl-2-butene + ...

Published
2018

5. Data-driven modeling for determination of asphaltene stability condition in oil system

Author

Amir H. Mohammadi, Arash Kamari, and Deresh Ramjugernath

Subjects
Adaptive neuro fuzzy inference system, Petroleum engineering, 020209 energy, General Chemical Engineering, Energy Engineering and Power Technology, 02 engineering and technology, General Chemistry, Geotechnical Engineering and Engineering Geology, Petroleum reservoir, Stability (probability), Data-driven, Fuel Technology, 020401 chemical engineering, Oil production, Phase (matter), 0202 electrical engineering, electronic engineering, information engineering, Environmental science, Precipitation, 0204 chemical engineering, Asphaltene
Abstract

Asphaltene precipitation is accounted as one of the most serious problems during oil production so that it can decrease the production of crude oil and cause the blockage of reservoir rock pores, etc. An accurate prediction of phase behaviour of asphaltene is therefore important in oil production industry. Accurate prediction of phase behaviour of asphaltene precipitation i.e. stability state of asphaltene precipitation in oilfields is greatly desirable. To this end, the applicability domains of the most important variables for the determination of the stability state of asphaltene precipitation viz. aromatic + resin and asphaltene + saturates have been specified by using decision tree (DT) algorithm. Next, adaptive neuro-fuzzy inference system (ANFIS) approach was implemented in order to determine the stability state of asphaltene precipitation using the efficient variables of aromatic + resin and asphaltene + saturates. The results obtained in the current study demonstrate that the models propos...

Published
2018

6. Modeling of the Properties of Gasoline and Petroleum Fractions Using a Robust Scheme

Author

Arash Kamari, Amir H. Mohammadi, Mehdi Sattari, and Deresh Ramjugernath

Subjects
business.industry, General Chemical Engineering, Reid vapor pressure, Energy Engineering and Power Technology, General Chemistry, Geotechnical Engineering and Engineering Geology, Least squares, Liquid fuel, Support vector machine, Fuel Technology, Petroleum industry, Octane rating, Biochemical engineering, Gasoline, business, Specific gravity, Mathematics
Abstract

Gasoline is one of the most recognized products of the petroleum industry due to its use as a liquid fuel worldwide. As a result, it is of great importance to accurately determine the properties of gasoline, so as to evaluate its quality. In this article, an effective mathematical and predictive strategy, namely least squares support vector machines (LSSVM) is applied to predict some gasoline properties, viz. specific gravity (SG), motor octane number (MON), research octane number (RON), and Reid vapor pressure (RVP). A comprehensive error analysis is also undertaken to compare the values predicted from the model with actual data which enables one to evaluate the performance of the model developed in this study. The results indicate that the model developed has reasonable accuracy and prediction capability. The correlation indices, R2, are 0.990, 0.933, 0.955, and 0.920 for SG, MON, RON, and RVP, respectively.

Published
2015

Catalog

Books, media, physical & digital resources
See catalog results